System for minimizing indoor infection risk and maximizing energy savings

ABSTRACT

A system and method for minimizing indoor infection risk and improving indoor air quality (IAQ) while maximizing energy savings. The system integrates occupancy detection and forecasting, outdoor weather conditions and forecasting, indoor infection risks and air quality modeling, any tunable air filtration, the clean air delivery rate, and any portable air cleaners. The system outputs the total amount of outdoor air intake, the air temperature of the supply air into the space, the supply air flow rate into the space, the operation mode of tunable air filtration/purification/disinfection, the operation mode of the in-room air cleaner, and space/room temperature set-points, and thus can serve as the central controller for an HVAC system.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to heating, ventilation, and airconditioning (HVAC) systems and, more specifically, to an approach forcontrolling an HVAC system to minimize indoor infection risk and theconcentrations of air contaminants of interest while maximizing energysavings through optimal ventilation, tunable air cleaning andtemperature control and their integration.

2. Description of the Related Art

An indoor environment is traditionally maintained by the heating,ventilation, and air conditioning (HVAC) system of using appropriatetemperature and ventilation controls. Conventional control approachesfocus on how to improve indoor environment through ventilation controlsor save energy through model predictive controls. These controlapproaches do not, however, take into account the need to minimizeinfection risk indoors and in order to reduce infection risk mostbuildings will simply increase outdoor air intake and/or install higherperformance air filters, both of which increases the energy consumptionof HVAC system. Accordingly, there is a need for an HVAC control systemthat can minimize indoor infection risk and the concentrations of aircontaminants of interest while maximizing energy savings through optimalventilation, tunable air cleaning and temperature control.

BRIEF SUMMARY OF THE INVENTION

The present invention is a system and method for minimizing indoorinfection risk and improving indoor air quality by reducingconcentrations of pollutants of interest while maximizing energy savingsthat integrates real-time occupancy detection and forecasting thatdetermines the current and future occupancy in a space (presence andpeople counting), outdoor weather condition forecasting that providescurrent and future outdoor weather information, indoor infection riskand air quality models that model the infection risk assuming animperfectly mixed realistic indoor air environment as well as theconcentrations of pollutants of interest for indoor air quality (IAQ)control, tunable air filtration/purification/disinfection technologieswith different efficiency that provides differentfiltration/purification/disinfection efficiency and clean air deliveryrate in the HVAC system for removing and diluting the virus-containingparticles, and any portable air purifier/cleaner devices that providethe infectious particle removal rate (or fresh air supply rate) by aircleaners. The present invention outputs the total amount of outdoor airintake, the air temperature of the supply air into the space, the supplyair flow rate into the space, the operation mode of tunable airfiltration/purification/disinfection, the operation mode of the in-roomair cleaner, and space/room temperature set-points, and thus can serveas the central controller for an entire HVAC system. The presentinvention can thus transform existing model predictive controlcapability to meet indoor infection risk control and indoor air quality(IAQ) requirements while maximize the energy savings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The present invention will be more fully understood and appreciated byreading the following Detailed Description in conjunction with theaccompanying drawings, in which:

FIG. 1 is a schematic of overview HVAC control system according to thepresent invention;

FIG. 2 is a schematic of a centralized MPC approach according to thepresent invention;

FIG. 3 is a schematic of a distributed MPC approach according to thepresent invention;

FIG. 4 is a high-level schematic of an offline learned MPC approachaccording to the present invention.

FIG. 5 is a schematic of a room/space thermal model according to thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring to the figures, wherein like numeral refer to like partsthroughout, there is seen in FIG. 1 a schematic of a building HVACsystem 10 including a control system 12 according to the presentinvention. System 12 includes three primary components, an indoor airquality model 14, an indoor infection risk model 16, and amulti-objective MPC 18. Multi-objective MPC receives data from all ofthe components of the HVAC system 10, calculates the appropriate controlsettings to implement a control strategy that minimizes indoor infectionrisk while maximizing energy savings through optimal ventilation,tunable air cleaning and temperature control, and outputs theappropriate control signal to the components of the HVAC system 10 toimplement the determined control strategy.

Control system 12 is based on a predictive control strategy to integrateoccupancy prediction, weather forecasting, indoor infection riskmodeling and building automation system. The disclosed overallarchitecture leverages building automation system information throughBACnet™, and develops MPC platform to minimize indoor infection riskwhile maximizing energy savings. The system architecture is designed tocompute and implement the optimized control strategy by forecastingfuture states of occupancy (presence and number of occupants),forecasting future states of local weather (temperature and solarradiation), forecasting future states of ambient air quality,forecasting future room heating and cooling loads, forecasting futurestates of room temperature and indoor air quality, communicating withbuilding automation system to obtain current and historical roomtemperature and indoor air quality, and communicating with buildingautomation system to obtain current operation mode of tunable airfiltration/purification/disinfection. All computations may be performedby cloud computing or by local computers or controllers or any types ofprocessors. System implement may be performed Internet of Things (IOTs)enabled components. Control system 12 is designed to fulfill thefollowing functions: optimize total amount of outdoor air intake amountand its schedule; optimize supply air temperature into the space and itsschedule; optimize supply air flow rate into the space and its schedule;optimize room/space temperature setpoint and its schedule; optimizeoperation mode of the tunable air filtration/purification/disinfectionand its schedule; and optimize operation mode of the in-room air cleaneron/off and its schedule.

Control system 12 includes a multi-objective model predictive controldesign, energy and IAQ management and optimization using modelingcomponents in overall model predictive control design. The overall modelpredictive control modeling components include an occupancy predictionmodel, a local weather (temperature and solar radiation forecasting)model, a local ambient air quality forecasting model, a room/spaceheating and cooling load forecasting model, an indoor infection riskmodel, an indoor air quality model, a building physics-based model, andan HVAC physics-based model. These components are seen in FIG. 1 .

Referring to FIG. 1 , hardware devices are depicted in oval shapes andinclude: in-room occupancy sensors; HVAC system including air handingunits (AHU), fans, pumps, and terminal units for large commercialbuildings, in-room sensors for temperature, relative humidity, particlesensors, TVOC, CO₂; tunable air filtration/purification/disinfectiontechnologies in AHU, in-room air cleaner; ambient air quality sensor;and the type of mask worn by a human occupant (if any).

The integrated control, energy and IAQ management, and optimizationsystem makes decisions based on: infection risk and IAQ models thatpredict the infection risk and the concentration of the indoor aircontaminants of interest based on outdoor air intake flow rate and airquality, total supply airflow rate, operation mode and efficiency of thetunable air filtration/purification/disinfection technologies, in roomair cleaner and type of mask; room/space load forecasting; occupants'thermal comfort preferences; occupants' schedule forecasting; room/spacecontrol state estimation; room/space air quality states; room/spaceoperational constraints; and weather forecasting. The integratedcontrol, energy and IAQ management, and optimization system communicatesthese decisions and constraints by dispatching heating, cooling andventilation set-points (e.g., thermostat set-points, air handling unitairflow rate, and supply air temperature set-points) to the local devicecontrollers. The system has decision-making capabilities to controlheating, cooling and ventilation system over a time horizon.

The system has an optimization engine that computes schedules of outdoorair intake airflow rate, supply air temperature, supply airflow rate,room set points for temperature, RH and target pollutant concentrations,operation mode of the tunable air filtration and operation mode of thein-room air cleaner based on the information it collects. The mainfeatures of the optimization engine include: a multi-objective modelpredictive control architecture, and a mixed integer programmingformulation to solve both continuous and discrete equipment controlssuch as in-room air cleaners. The disclosed system has three differentapproaches to solve the MPC problem and implement control outputs inreal-time. The user can choose any of those three approaches based onthe availability of onsite computing resources.

First, is an online centralized MPC at each AHU level as shown in FIG. 2. In this approach, the optimization is solved at a supervisory level,and implemented at each AHU level.

Second, an online distributed MPC at zone level as shown in FIG. 3 . Inthis approach, the optimization is solved at each room/zone level andimplemented at room/zone level.

Third, offline learning as shown in FIG. 4 . In this approach, thesystem learns a large possible control outputs either using approaching1 or approach 2 based on historic data. Such learning can be conductedevery a few weeks. During the online implementation, the algorithms willlook up in the database for similar boundary conditions (e.g. weather,air quality, number of people) and find corresponding control outputs.

Component Level Modeling according to the present invention includes aRoom/Space Thermal Model, where A room^(l) can be represented using a3R2C model, as seen in FIG. 5

From the 3R2C model, the thermal dynamics of the room can be representedas follows:

$\begin{matrix}{{\overset{.}{T}}_{wall} = {\frac{T_{amb} - T_{wall}}{{CR}_{2}} + \frac{T_{zone} - T_{wall}}{{CR}_{1}} + \frac{{\overset{.}{Q}}_{sol}}{C}}} & \left( {1\; a} \right) \\{{\overset{.}{T}}_{zone} = {\frac{T_{wall} - T_{zone}}{C_{zone}R_{1}} + \frac{T_{amb} - T_{zone}}{C_{zone}R_{win}} + \frac{{\overset{.}{Q}}_{int} + {\overset{.}{Q}}_{room}}{C_{zone}}}} & \left( {1\; b} \right)\end{matrix}$

Where {dot over (Q)}_(room) is the total cooling or heating loadinjected into the room by the HVAC system, calculated as:{dot over (Q)} _(room) =c·{dot over (m)} _(room)(T _(supply,room) −T_(set))  (2)Further, the mass flow rate and air supply temperature to the room arefunctions of the damper position δ_(damp), and reheating coil valveposition δ_(vav_rh);{dot over (m)} _(room) =f _(damp)(δ_(damp))  (3)T _(supply,room) =f _(T)(δ_(vav_rh),δ_(damp))  (4)

Substituting (3), (4) into (2) results in the room cooling or heatingload being expressed in terms of three control variables δ_(vav_rh),δ_(damp),T_(set);{dot over (Q)} _(room) =f _(Q)(δ_(vav_rh),δ_(damp) ,T _(set))=c·f_(damp)(δ_(damp))·(f _(T)(δ_(vav_rh),δ_(damp))−T _(set))  (5)

Thus, the dynamics of the room (1) can be expressed in a semi-linearform:

$\begin{matrix}{{\overset{.}{\mspace{79mu} T}}_{wall} = {\frac{T_{amb} - T_{wall}}{{CR}_{2}} + \frac{T_{zone} - T_{wall}}{{CR}_{1}} + \frac{{\overset{.}{Q}}_{sol}}{C}}} & \left( {6\; a} \right) \\{{\overset{.}{T}}_{zone} = {\frac{T_{wall} - T_{zone}}{C_{zone}R_{1}} + \frac{T_{amb} - T_{zone}}{C_{zone}R_{win}} + \frac{{\overset{.}{Q}}_{int} + {f_{Q}\left( {\delta_{{vav}\;\_\;{rh}},\delta_{damp},T_{set}} \right)}}{C_{zone}}}} & \left( {6\; b} \right)\end{matrix}$

These room dynamics can be written in state space form as follows:{dot over (x)} _(l) ^(t) =A _(l) x _(l) ^(t) =B _(l) f(u _(l) ^(t))=E_(l) w _(l) ^(t) ∀l∈

,∀t∈

  (7)where x_(l) ^(t)=[T_(wall) ^(t) T_(zone) ^(t)]_(l) ^(T) is the state ofthe room (i.e., wall and zone temperatures);f(u_(l) ^(t))=f_(Q)(δ_(vav_rh), δ_(damp), T_(set))_(l) ^(t) is anonlinear function of the three room-specific control input variables(VAV reheating coil position, damper position, setpoint temperature);w_(l) ^(t)=[T_(amb) ^(t) {dot over (Q)}_(sol) ^(t) {dot over (Q)}_(int)^(t)]_(l) ^(T) is the uncontrollable input at time t, comprised ofambient temperature, heat gains due to solar radiation, and internalheat gains due to occupants, lights, and equipment; and the systemmatrices A_(l), B_(l), E_(l) are as follows:

$\begin{matrix}{{A_{l} = {\begin{bmatrix}{{- \frac{1}{C}}\left( {\frac{1}{R_{1}} + \frac{1}{R_{2}}} \right)} & \frac{1}{{CR}_{1}} \\\frac{1}{C_{zone}R_{1}} & {{- \frac{1}{C_{zone}}}\left( {\frac{1}{R_{1}} + \frac{1}{R_{win}}} \right)}\end{bmatrix}l}}{B_{l} = {\begin{bmatrix}0 \\\frac{\mu}{C_{zone}}\end{bmatrix}l}}{E_{l} = {\begin{bmatrix}\frac{1}{{CR}_{2}} & \frac{1}{C} & 0 \\\frac{1}{C_{zone}R_{win}} & 0 & \frac{1}{C_{zone}}\end{bmatrix}l}}} & (8)\end{matrix}$where R₁, R₂, R_(win), C, C_(zone) are the building thermal resistanceand capacitance values and μ is the coefficient of performance of theAHU.

The HVAC model is based on the AHU which serves the entire buildingprovides a total cooling or heating load according to its mass flow rateand temperature differential:{dot over (Q)} _(AHU) =c·{dot over (m)} _(AHU)(T _(supply,AHU) −T_(mix))  (9)

The mass flow rate is a mixture of outdoor air intake and return air,with a being the fraction made up by outdoor air or outdoor air intakepercentage:{dot over (m)} _(AHU) =α·{dot over (m)} _(out)+(1−α)·{dot over (m)}_(return)  (10)Therefore by substituting (10) into (9), the air side total cooling orheating load can be expressed in terms of AHU-specific control variablesu_(AHU)=[T_(supply,AHU), {dot over (m)}A_(HU), α]:{dot over (Q)} _(AHU) =f _(AHU)(T _(supply,AHU),α)=c·(α·{dot over (m)}_(out)+(1−α)·{dot over (m)} _(return))·(T _(supply,AHU) −T _(mix))  (11)

The AHU model and room models are coupled through a mass flow balance(i.e., the total AHU flow rate is the sum of all n room flow rates):{dot over (m)} _(AHU) ={dot over (m)} ₁ +{dot over (m)} ₂ + . . . +{dotover (m)} _(l) + . . . +{dot over (m)} _(n)  (12)

Finally, the total HVAC fan power use is calculated as a function of thetotal flow rate:p _(fan) =f _(fan)({dot over (m)} _(AHU))  (13)

The Infection Risk Model is used to determine infection risk, which canbe quantified by the well-known Wells-Riley equation [2]:

$\begin{matrix}{P = {\frac{N_{C}}{N_{S}} = {1 - e^{- \frac{Iqpt}{V\;\Lambda}}}}} & (14)\end{matrix}$

where N_(C)=the number of new cases in the space; N_(S)=the number ofsusceptible people; I=the number of virus carrier at the start of theexposure period; q=the infectious quantum generation rate per viruscarrier (quanta/h per person); p=pulmonary ventilation rate (m³/h);t=exposure time (h); V=space air volume (m³); Λ=the fresh air changerate in the room (l/h).

Equation (5) establishes the relationship between the probability ofinfection (i.e., infection risk) in a perfectly mixed air space and theaverage viral dose exposure as:P=1−e ^(−D)  (15)

Where D=viral dose exposure (quanta). Note that 1 quantum is the amountof viral dose exposure needed to result in a probability of infection of63% per Equation (5). The dose exposure for an individual in the spaceis calculated as follows:

$\begin{matrix}{D = {{- R_{S}}R_{l}\frac{Iqpt}{V\;\Lambda}}} & (16)\end{matrix}$

Where R_(S) and R_(L)=fraction of infectious particles passing throughthe mask worn by the virus carriers and the succeptibles, respectively;and Λ=the “total equivalent” clean air change rate for the space interms of infection virus dilution due to the air cleaning devices incentral HVAC system and/or within the room space as well as that fromthe outdoor air intake flow rate (outdoor ventilation rate). Equations(6) and (7) are used to predict the risk of infection for an individualin an indoor space accounting for the effects of ventilation, HVACsystem supply and/or recirculated air cleaning, in-room air cleaners,and mask wearing [3].

The fraction of infectious particle penetrated through the mask orrespirator for susceptible (R_(S)) and infected (R_(I)) population canbe calculated by Eqn. 8 and 9, respectively. And both depend on the maskfiltration efficiency (η_(S) or η_(I)). The penetration fraction (R)equals 1 when no mask or respirator is used during the exposure period.An additional fractional factor (f_(R)) is multiplied by the originalfiltration efficiency of the mask to represent the fraction of timeusing a mask/respirator over the entire exposure period. It equals 1when the mask is worn during the entire exposure period.R _(S)=1−f _(R,S)η_(S)  (17)R _(I)=1−f _(R,I)η_(I)  (18)

The “total effective” air change rate depends on the “effective” airchange rates due to ventilation rate (λ_(vent)), pathogen inactivationrate (k_(UV)) by ultraviolet germicidal irradiation (UVGI) systems,infectious particle deposition rate (k_(deposition)) and pathogennatural inactivation rate in the air (k_(inactivation)), as shown inEqn. 10. The “effective” air change rate includes the air change ratedue to fresh air supply rate by the HVAC system (λ_(HVAC) or {dot over(m)}_(AHU)), natural ventilation rate (λN_(V)) and infectious particleremoval rate by air purifiers (k_(purifier)). The fraction (f) ofoperation time over the entire exposure period is applied to each termin Eqn. 11 to determine the net overall ventilation rate. Thefresh/clean air change rate supplied by the HVAC system (λ_(HVAC) or{dot over (m)}_(AHU)) includes the outdoor part and the recirculatedpart. The recirculated fresh/clean air change rate (Eqn. 12) depends onthe recirculated air change rate (λ_(recirculated)) and the filtrationefficiency of the filters in the HVAC system for the virus-containingparticles (η_(filter)). The natural ventilation air change rate (λ_(NV))results from the airflows through openings and cracks on the buildingenvelope.Λ=λ_(vent) +f _(UV) k _(UV) +k _(deposition) +k _(inactivation)  (19)λ_(vent) =f _(HVAC) k _(HVAC) +f _(NV)λ_(NV) +f _(purifier) k_(purifier)  (20)λ_(HVAC)=λ_(outdoor)+λ_(recirculated)η_(filter)  (21)

An in-room air purifier can supply additional fresh/clean air to thespace. The infectious particle removal rate (or fresh air supply rate)by air purifiers (k_(purifier)) can be estimated by its airflow rate(λ_(purifier)) and filter efficiency (η_(purifier)), or based on itsclean air delivery rate (CADR) and room volume (V):

$\begin{matrix}{k_{purifier} = {{\lambda_{purifier}\eta_{purifier}} = \frac{CADR}{V}}} & (22)\end{matrix}$The actual ventilation rate (λ_(vent)) is a spatial-variable thatdepends on the particular location in the space. Thus, an additionalfactor is applied to the original equation in Eqn. 11 to adapt it toimperfect mixing scenarios. The infectious particle removal rates due toUVGI systems, deposition, and natural inactivation are assumed to beuniform in the whole space. Therefore, these terms do not have to bemodified for imperfect mixing.

The pathogen removal rate by the UVGI system depends on the fraction ofUVGI operation time (f_(UV)) and the pathogen inactivation rate due toUV irradiation (k_(UV)). The infectious particle deposition rate(k_(deposition)) relies on an approximate estimate of gravitationalsettling (Eqn. 14) from Nicas et al. [4], which depends on the particlediameter (d_(p)) and room height (H). It is assumed that the depositedparticles will not be resuspended into the air space again.

$\begin{matrix}{k_{deposition} = \frac{0.108\mspace{11mu}{d_{p}^{2}\left( {1 + \frac{0.166}{d_{p}}} \right)}}{H}} & (23)\end{matrix}$

The pathogen natural inactivation rate is not considered, in partbecause of the lack of existing data on the size-resolved naturalinactivation rate of SARS-CoV-2 and in part because quanta generationrates (q), when back-calculated using Eqn. 1, will inherently accountfor any inactivation that occurred during the case study period [5].

The IAQ model predicts the concentrations of pollutants of interest inthe occupied zone and estimate the health risks associated with theoccupant exposure to the various pollutants. The concentration of anindividual pollutant (i) of interest is governed by the following zonemass balance equation:

$\begin{matrix}{{V\frac{d\; C}{d\; t}} = {{A_{e}{E(t)}} + {Q\left( {C - C_{s}} \right)} + {A_{s}{S(t)}}}} & (24)\end{matrix}$Where V=zone volume (m³); C=concentration (ug/m³); A_(e)=emissionsurface area (m²); E(t) emission factor (i.e., emission rate per unitsurface area, ug/h/m²); Q=supply air volumetric flow rate to the zone(m³/h) which is

$\frac{{\overset{.}{m}}_{room}}{\rho};$C_(s)=concentration of the supply air which include the effects ofventilation and air cleaning in the central HVAC system (ug/m³);A_(s)=Area of sink surfaces (m²); S(t) sorption/filtration/cleaning rateby sinks including in-room air cleaners (ug/h/m²).

The model is further extended to multizone buildings to account for theinter-zone pollutant transport [6]. The model is used to predict theconcentrations over a time of horizon in the MPC model, and thepredicted concentrations are compared to pre-established thresholdlimits for each pollutant of interest such as formaldehyde, PM2.5, CO₂,etc. for maintaining satisfactory IAQ in the buildings, or forestimating the health risk associated with the cumulative exposure tothe pollutants.

The multi-objective model predictive control can implement a centralizedMPC approach where a joint optimization objective can be designed tominimize the weighted sum of total AHU fan energy, total air sideheating/cooling energy in an AHU, total zone reheating energy (ifapplicable), zone infection risk, and the concentrations of pollutantsof interest (if applicable) for a prediction horizon h:J ^(t)=ρ_(f) p _(fan) ^(t)+ρ_(coil) p _(AHU) ^(t)+ρ_(rhz) p _(rhz)^(t)+ρ_(risk) p _(risk) ^(t)+ρ_(c) p _(c) ^(t)  (25)Each term is weighted by a cost scalar ρ(·), and the overall objectiveis a time average of the sum of the five weighted terms. These weightsand those in subsequent objective functions are tuned to achieve optimalcontrol performance based on training data. Once tuned, the weights mayremain static unless the underlying system models (i.e., room or AHUmodels) are changed. In addition, any cost function from utilitycompanies could be added into all energy terms in Eq. (25). Thus, acentralized MPC controller will solve the following multi-objectiveproblem for all room at an AHU level, over some prediction horizon h:

$\begin{matrix}{{{minimize}\mspace{14mu}{\sum\limits_{l = 1}^{h}\;{j^{t}\left( {u_{{AHU}\;\_\;{central}}^{t},u_{t\;\_\;{central}}^{t}} \right)}}}{{{subject}\mspace{14mu}{to}\mspace{14mu}(7)},(11),(12),(13),(14),(24)}{{{u_{AHU}^{\min} \leq u_{AHU}^{t} \leq {u_{AHU}^{\max}\mspace{14mu}{\forall t}}} = 1},\ldots\mspace{14mu},h}{{u_{l}^{\min} \leq u_{l}^{t}},{{\leq {u_{l}^{\max}\mspace{14mu}{\forall l}}} = 1},\ldots\mspace{14mu},n,\mspace{14mu}{{\forall t} = 1},\ldots\mspace{14mu},h}{{x_{l}^{\min} \leq x_{l}^{t}},{{\leq {x_{l}^{\max}\mspace{14mu}{\forall l}}} = 1},\ldots\mspace{14mu},n,\mspace{14mu}{{\forall t} = 1},\ldots\mspace{14mu},h}} & (26)\end{matrix}$where u_(AHU_central) ^(t)=[T_(supply,AHU) ^(t), {dot over (m)}_(AHU)^(t), α^(t), η_(filter) ^(t)] are new control variables at the AHUlevel, and where u_(l_central) ^(t)=[δ_(vav) _(rh) _(l) ^(t), δ_(damp,l)^(t), T_(set,l) ^(t), η_(purifier,l) ^(t), R_(s,l), R_(l,l)] are newcontrol variables at each room l.

In the distributed MPC approach, a joint optimization objective can bedesigned to minimize the weighted sum of total air side heating/coolingenergy in each room, total zone reheating energy (if applicable), zoneinfection risk, and the concentrations of pollutants of interest (ifapplicable) for a prediction horizon h, for each room l:J _(l) ^(t)=ρ_(room) p _(room) ^(t)+ρ_(rhz) p _(rhz) ^(t)+ρ_(risk) p_(risk) ^(t)+ρ_(c) p _(c) ^(t)  (27)

Each term is weighted by a cost scalar and the overall objective is atime average of the sum of the four weighted terms. Thus, for each room,a MPC controller will solve the following multi-objective problem oversome prediction horizon h:

$\begin{matrix}{{{minimize}\mspace{14mu}{\sum\limits_{t = 1}^{h}\;{j^{t}\left( u_{l\;\_\;{dist}}^{t} \right)}}}{{{subject}\mspace{14mu}{to}\mspace{14mu}(7)},(11),(14),(24)}{{u_{l}^{\min} \leq u_{l}^{t}},{{\leq {u_{l}^{\max}\mspace{14mu}{\forall l}}} = 1},\ldots\mspace{14mu},n,\mspace{14mu}{{\forall t} = 1},\ldots\mspace{14mu},h}{{x_{l}^{\min} \leq x_{l}^{t}},{{\leq {x_{l}^{\max}\mspace{14mu}{\forall l}}} = 1},\ldots\mspace{14mu},n,\mspace{14mu}{{\forall t} = 1},\ldots\mspace{14mu},h}} & (28)\end{matrix}$where u_(l_dist) ^(t)=[λ_(outdoor,l) ^(t), δ_(vav) _(rh) _(l) ^(t),δ_(damp,l) ^(t), T_(set,l) ^(t), η_(purifier,l) ^(t), R_(s,l), R_(l,l)]are new control variables at each room l. λ_(outdoor,l) ^(t) is therequired outdoor air portion of the total fresh air into each zone/rooml.

As described above, the present invention may be a system, a method,and/or a computer program associated therewith and is described hereinwith reference to flowcharts and block diagrams of methods and systems.The flowchart and block diagrams illustrate the architecture,functionality, and operation of possible implementations of systems,methods, and computer programs of the present invention. It should beunderstood that each block of the flowcharts and block diagrams can beimplemented by computer readable program instructions in software,firmware, or dedicated analog or digital circuits. These computerreadable program instructions may be implemented on the processor of ageneral purpose computer, a special purpose computer, or otherprogrammable data processing apparatus to produce a machine thatimplements a part or all of any of the blocks in the flowcharts andblock diagrams. Each block in the flowchart or block diagrams mayrepresent a module, segment, or portion of instructions, which comprisesone or more executable instructions for implementing the specifiedlogical functions. It should also be noted that each block of the blockdiagrams and flowchart illustrations, or combinations of blocks in theblock diagrams and flowcharts, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

What is claimed is:
 1. A system for providing control strategy for aheating, ventilation, and air condition (HVAC) system of a building,comprising: a controller having an input for receiving a first signalcomprising data representing ambient weather information, a secondsignal comprising data representing an occupancy level in a location, athird signal comprising data representing a temperature of the location,a fourth signal comprising data representing an amount of outdoor airintake into the location, and fifth signal comprising data representinga quality of ambient air in the location and an output for sending aplurality of control signals governing operation of a plurality of HVACsystem components that may be connected to the controller; a databaseassociated with the controller and configured to record the datarepresenting ambient weather information, the data representing theoccupancy level, the data representing the temperature, and the datarepresenting the amount of outdoor air intake over time; a processorassociated with the controller and programmed to generate a predictionof occupancy level over a predetermined upcoming period of time based onthe recorded data representing the occupancy level, to generate anestimation of carbon dioxide levels in the location based on the datarepresenting occupancy level over time and the data representing theamount of outdoor air intake over time, and to generate a forecast ofambient air quality based on the recorded data representing ambientweather information over time and the recorded data representing thequality of ambient air in the location over time; wherein the processoris programmed to dynamically estimate an amount of risk of infection inthe location based on the prediction of occupancy level and the forecastof ambient air quality; wherein the processor is programmed to determinean amount of energy required to operate the HVAC system componentsconnected to the controller; wherein the processor is programmed todetermine how to operate the plurality of HVAC system components tominimize the amount of energy required to operate the HVAC systemcomponents while minimizing the amount of risk of infection in thelocation; and wherein the processor is programmed to cause thecontroller to send the plurality of control signals governing operationof the plurality of HVAC system components based on the determination ofhow to operate the plurality of HVAC system components to minimize theamount of energy required to operate the HVAC system components whileminimizing the amount of risk of infection in the location.
 2. Thesystem of claim 1, wherein the plurality of control signals includes anair handling unit control signal.
 3. The system of claim 2, wherein theair handling unit control signal will cause a change in outside airintake.
 4. The system of claim 3, wherein the air handling unit controlsignal will cause a change in supply air flow rate.
 5. The system ofclaim 4, wherein the air handling unit control signal will cause achange in supply air temperature.
 6. The system of claim 1, wherein theplurality of control signals includes an air filtration unit signal. 7.The system of claim 1, wherein the processor is located remotely fromthe controller and can communicate with the controller over theinternet.
 8. The system of claim 7, wherein the database is locatedremotely from the controller.
 9. The system of claim 1, wherein theprocessor is programmed to determine how to operate the plurality ofHVAC system components to minimize the amount of energy required tooperate the HVAC system components while minimizing the amount of riskof infection in the location by implementing a multi-objective modelpredictive control algorithm.
 10. The system of claim 9, wherein themulti-objective model predictive control algorithm considers a dynamicindoor air quality model and a coil load of any air handling unitconnected to controller.
 11. A method of operating a plurality of HVACsystem components to minimize the amount of energy required to operatethe HVAC system components while maximizing indoor air quality,comprising the steps of: receiving a first signal comprising datarepresenting ambient weather information, a second signal comprisingdata representing an occupancy level in a location, a third signalcomprising data representing a temperature of the location, a fourthsignal comprising data representing an amount of outdoor air intake intothe location, and fifth signal comprising data representing a quality ofambient air in the location; recording the data representing ambientweather information, the data representing the occupancy level, the datarepresenting the temperature, and the data representing the amount ofoutdoor air intake over time; using a processor to generate a predictionof occupancy level over a predetermined upcoming period of time based onthe recorded data representing the occupancy level, to generate anestimation of carbon dioxide levels in the location based on the datarepresenting occupancy level over time and the data representing theamount of outdoor air intake over time, and to generate a forecast ofambient air quality based on the recorded data representing ambientweather information over time and the recorded data representing thequality of ambient air in the location over time; using the processor todynamically estimate an amount of risk of infection in the locationbased on the prediction of occupancy level and the forecast of ambientair quality; using the processor to determine an amount of energyrequired to operate the HVAC system components connected to thecontroller; using the processor to determine how to operate theplurality of HVAC system components to minimize the amount of energyrequired to operate the HVAC system components while minimizing theamount of risk of infection in the location; and causing a controllerhaving an output for sending a plurality of control signals governingthe operation of a plurality of HVAC system components that may beconnected to the controller to send the plurality of control signalsgoverning the operation of the plurality of HVAC system components basedon the determination of how to operate the plurality of HVAC systemcomponents to minimize the amount of energy required to operate the HVACsystem components while minimizing the amount of risk of infection inthe location.
 12. The method of claim 11, wherein the plurality ofcontrol signals includes an air handling unit control signal.
 13. Themethod of claim 12, wherein the air handling unit control signal willcause a change in outside air intake.
 14. The method of claim 13,wherein the air handling unit control signal will cause a change supplyair flow rate.
 15. The method of claim 14, wherein the air handling unitcontrol signal will cause a change in supply air temperature.
 16. Themethod of claim 11, wherein the plurality of control signals includes anair filtration unit signal.
 17. The method of claim 11, wherein theprocessor is located remotely from the controller and can communicatewith the controller over the internet.
 18. The method of claim 17,wherein the step of recording the data is accomplished by a databaselocated remotely from the controller.
 19. The method of claim 11,wherein the processor is programmed to determine how to operate theplurality of HVAC system components to minimize the amount of energyrequired to operate the HVAC system components while minimizing theamount of risk of infection in the location by implementing amulti-objective model predictive control algorithm.
 20. The method ofclaim 19, wherein the multi-objective model predictive control algorithmconsiders a dynamic indoor air quality model and a coil load of any airhandling unit connected to controller.